Transactions on Computational Science XXV

Enhancement of infrared (IR) images is a perplexing task. Infrared imaging finds its applications in military and defense related problems. Since IR devices capture only the heat emitting objects, the visualization of the IR images is very poor. To improve the quality of the given IR image for better perception, suitable enhancement routines are required such that contrast can be improved that suits well for human visual system. To accomplish the task, a fuzzy set based enhancement of IR images is proposed in this paper. The proposed method is adaptive in nature since the required parameters are calculated based on the image characteristics. Experiments are carried out on standard benchmark database and the results show the efficacy of the proposed method.

In this work, we propose a novel approach in which a system autonomously composes dance sequences from previously taught dance moves with the help of the well-known differential evolution algorithm. Initially, we generated a large population of dance sequences. The fitness of each of these sequences was determined by calculating the total inter-move transition abruptness of the adjacent dance moves. The transition abruptness was calculated as the difference of corresponding slopes formed by connected body joint co-ordinates. By visually evaluating the dance sequences created, it was observed that the fittest dance sequence had the least abrupt inter-move transitions. Computer simulation undertaken revealed that the developed dance video frames do not have significant inter-move transition abruptness between two successive frames, indicating the efficacy of the proposed approach. Gestural data specific of dance moves is captured using a Microsoft Kinect sensor. The algorithm developed by us was used to fuse the dancing styles of various ‘Odissi’ dancers dancing to the same rasa (theme) and tala (beats) and loy (rhythm). In future, it may be used to fuse different forms of dance.

Maritime surveillance represents a challenging scenario for moving object segmentation due to the complexity of the observed scenes. The waves on the water surface, boat wakes, and weather issues contribute to generate a highly dynamic background. Moving object segmentation using change detection under maritime environment is a challenging problem for the maritime surveillance system. To address these issues, a fast and robust moving object segmentation approach is proposed which consist of seven steps applied on given video frames which include wavelet decomposition of frames using complex wavelet transform; use of change detection on detail coefficients (LH, HL, HH); use of background modeling on approximate co-efficient (LL sub-band); cast shadow suppression; strong edge detection; inverse wavelet transformation for reconstruction; and finally using closing morphology operator. For dynamic background modeling in the water surface, we have used background registration, background difference, and background difference mask in the complex wavelet domain. For shadow detection and suppression problem in water surface, we exploit the high frequency sub-band in the complex wavelet domain. A comparative analysis of the proposed method is presented both qualitatively and quantitatively with other standard methods available in the literature for seven datasets. The various performance measures used for quantitative analysis include relative foreground area measure (RFAM), misclassification penalty (MP), relative position based measure (RPM), normalized cross correlation (NCC), Precision (PR), Recall (RE), shadow detection rate (SDR), shadow discrimination rate, execution time and memory consumption. Experimental results indicate that the proposed method is performing better in comparison to other methods in consideration for all the test cases as well as addresses all the issues effectively.

The subjective evaluation method of usability is costly and time-consuming, and is sometimes more unreliable data than objective evaluation method because of the subjective view. On the other hand, the objective evaluation method is traditionally useful and reliable, but expensive. Further, this method is often not feasible, as acquiring the operation logs of many electrical products can be difficult. To overcome current limitations, we propose an objective evaluation method of usability that is applicable to various types of interfaces, such as those of actual electrical products or reproduced interfaces on a touch screen. Our proposed method involves extracting recorded fingertip movements of users during operation via image processing and then evaluating usability based on measurable parameters. Experimental results demonstrate that the proposed method was able to identify problems with usability even when the traditional objective evaluation method could not.

Medical image fusion is the process of registering and combining multiple images from single or multiple imaging modalities. It helps to improve the imaging quality and reduces the redundancy, which improves the clinical applicability of medical images for diagnosis. The idea is to improve the content of an image by fusing images of multiple modalities viz. positron emission tomography (PET), computerized tomography (CT), single-photon emission computerized tomography (SPECT), magnetic resonance imaging (MRI) etc. Registration is an important step before fusion. In general, the problem of image registration can be identified as the determination of geometric transformations between the respective source image and target image.

In this paper, we have used Daubechies wavelet and near fuzzy set for registration of multi-modal images and a new pixel-level multi-modal technique for medical image fusion based on complex wavelet and near set approach. Our proposed technique produces excellent fused images and minimizes fusion associated problems giving a high quality image, restoring almost every information of the source images. In this work, we have considered various image modalities like PET, CT, SPECT and MRI. The experimental evaluation for various benchmark images shows that the proposed fusion framework can generate excellent fused images as compared to the other state-of-the-art methods.

With the increasing demand for automatic security systems capable of recognizing people from a far distance and with as little cooperation as possible, gait as a behavioral biometric has recently gained a lot of attention. It is a remotely observable and unobtrusive biometric. However, the complexity and the high variability of gait patterns limit the power of gait recognition algorithms and adversely affect their recognition rates in real applications. With the goal to improve the performance of gait recognition systems without investing into costly and complex algorithms, we introduce a novel multimodal gait recognition system that combines the gait behavioral patterns of the subjects with the social patterns of their activities. For this purpose, a standard gait recognition system is implemented. A novel context matcher module is added to the system that provides a framework for modeling, learning, extracting and matching the contextual behavioral patterns. The learning of gait and behavioral patterns and clustering of results is performed. This allows grouping the subjects into similar profiles for faster recognition and enrollment. The results from two modules: context matcher and gait recognition are fused in the multi-modal decision making. The experiments on HumanID Challenge dataset are performed to validate that recognition rate improves using the combination of video context and gait recognition method even in the presence of low quality data.

In this paper, clustering issue of sensor network is addressed. These types of network is a large wireless network, consisting of tiny, low cost sensors which senses phenomenal data, such as light, temperature, pressure, sound, etc. The sensors are small hardware devices, which sense using their sensing unit and measure physical conditions of the area being monitored. There are number of applications in which a hierarchical based network is highly demanded and key concept of such network is clustering. We have proposed clustering heuristic on the basis of ranks and weights assignment based protocol. This approach considers not only residual energy but also node’s degree and distance of nodes with base station. The node which has higher weight will be chosen as a cluster head. The objective of this approach is to have balance distribution of clusters, enhance lifetime and better efficiency than traditional protocols. The same approach is also applied for multi hop clustering. Results show the efficacy of proposed approach in terms of energy consumption of the sensor nodes and longevity of the network.

A method to implement a digital multilayer perceptron (DMLP) in an FPGA is proposed, where the DMLP is tolerant to simultaneous weight and neuron faults. It has been shown in [1] that a multilayer perceptron (MLP) which has successfully trained using the deep learning method is tolerant to multiple weight and neuron faults where the weight faults are between the hidden and output layers, and the neuron faults are in the hidden layer. Using this fact, a set of weights in the trained MLP is installed in an FPGA to cope with these faults. Further, the neuron faults in the output layer are detected or corrected using SECDED code. The above process is done as follows. The generator developed by us automatically outputs a VHDL source file which describes the perceptron using a set of weight values in the MLP trained by the deep learning method. The VHDL file obtained is input to the logic design software Quartus II of Altera Inc., and then, implemented in an FPGA. The process is applied to realizing fault-tolerant DMLPs for character recognitions as concrete examples. Then, the perceptrons to be made fault-tolerant and corresponding non-redundant ones not to be made fault-tolerant are compared in terms of not only reliability and fault rate but also hardware size, computing speed and electricity consumption. The data show that the fault rate of the fault-tolerant perceptron can be significantly decreased than that of the corresponding non-redundant one.

This paper is the full version of [2].

In this paper expressions of outage probability under Nakagami-\(m\) fading for Coded Cooperative Communication have been developed. Performance of outage has been simulated for various factors such as allocated Rate, cooperation level, mean channel SNR and number of relays. Diversity order has been calculated for Coded Cooperative Communication with multiple relays. Outage probability expression for Space Time Cooperation with multiple partners under Nakagami-m have also been developed and analyzed. A critical comparative analysis is done for No Cooperation, Coded Cooperation and Space Time Cooperation. Simulation results highlight the benefits of Coded Cooperation over the other two schemes under the slow fading circumstances.

The life cycle of an urban railway system is about thirty years, and therefore, any small improvement in operation, results huge savings. The daily operation follows a headway distribution, which itself computed based on the traffic volume. Passing the block sections and stopping in stations are always contaminated with disruptions. The disruptions affect the traffic especially when the headway is in minimum. In this paper, computing the exact practical travel and dwell times is studied. At the first stage a formula is proposed to compute the remained disruptions at the end of the last period in minimum headway. It is supposed that the travel and dwell times take values according to a symmetric distribution. The amount of supplementary times to reach the desired reliability is defined based on the probability of non-absorbed disruptions at the end of the last period. It is concluded that as the number of disrupted travel and dwell times increases the amount of required supplementary times to reach the same level of reliability, increases but in a descending rate. This finding improves the current method to reach the reliability in urban railway operation plans. Finally, the Karaj Metro Line 2 is studied and analyzed as the case study.